Štefica Mrvelj
University of Zagreb, Faculty of Transport and Traffic Sciences
Marko Matulin
University of Zagreb, Faculty of Transport and Traffic Sciences
Sergo Martirosov
University of West Bohemia
This paper reports on the results of subjective testing of user Quality of Experience (QoE) for omnidirectional video (ODV) streaming quality. The test was conducted among 20 test subjects who watched three ODVs using a Head Mounted Display (HMD) system. The length of the videos was between two and three minutes. The first video was used for training purposes and contained no quality degradations. The quality of the other two ODVs was degraded by manipulating the resolution or by introducing different frame drop patterns. While watching the pre-prepared videos the subjects indicated if they noticed the changes in the quality and then rated it. After watching each video, the subjects completed a separate questionnaire, which evaluated their level of enjoyment and discomfort with the video. The results showed that the degradation of both objective parameters (video resolution and frame rate) impacted the subjects’ perception of quality; however, the impact was somewhat alleviated in ODV which contained dynamic scenes and fast camera movements.
Ericsson Consumerlab. Merged Reality: Understanding How Virtual and Augmented Realities Could Transform Everyday Reality. Ericsson; 2017. Available from: https://www.ericsson.com/en/trends-and-nsights/consumerlab/
consumer-insights/reports/merged-reality [Accessed June 2019].
Mrvelj Š, Matulin M. Impact of Packet Loss on the Perceived Quality of UDP-based Multimedia Streaming: A Study of User Quality of Experience in Real-life Environments. Multimedia Systems. 2018;24(1): 33-53. Available from: doi:10.1007/s00530-016-0531-8
Matulin M, Mrvelj Š. Modelling User Quality of Experience from Objective and Subjective Data Sets using Fuzzy Logic. Multimedia Systems. 2018;24(6): 645-667 Available from: doi:10.1007/s00530-018-0590-0
https://www.fpz.unizg.hr/qoe4vr/ [Accessed September 2019].
Cisco. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2017-2022. White Paper. Cisco; 2019. Available from: https://www.cisco.com/c/en/us/solutions/collateral/service-pr
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Editors: Marko Matulin, PhD, Dario Babić, PhD, Marko Ševrović, PhD
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